Determining Protein Function Research Articles

RecGOBD: Accurate Recognition of Gene Ontology Related Brain Development Protein Functions through Multi-Feature Fusion and Attention Mechanisms

Abstract Motivation Protein function prediction is important in bioinformatics, driven by the increasing availability of protein sequence data from high-throughput sequencing technologies. Traditional methods for determining protein functions are costly and time-consuming, highlighting the need for computational approaches. Deep learning models are powerful tools in this area, but many are not optimized for brain development-related datasets. Understanding protein functions in brain development is essential for studying neurodevelopmental disorders. To address this, we developed RecGOBD (Recognition of Gene Ontology-related Brain Development protein function), designed to predict protein functions related to key brain development processes. Result RecGOBD focuses on ten critical Gene Ontology (GO) terms associated with brain development, extracting and embedding the protein sequences linked to these terms. Using advanced pre-trained models, we generated embeddings that capture both sequence and structural information, and applied attention mechanisms to align them with relevant GO terms. RecGOBD’s category attention layer improves prediction accuracy for brain development-related terms. Evaluated against five models using AUROC, AUPR, and Fmax metrics, RecGOBD outperformed in all measures. We also applied the model to predict protein functions related to autism spectrum disorder and analyzed how protein site mutations affect GO term changes. These results highlight RecGOBD’s potential for advancing protein function prediction, particularly in brain development, offering valuable insights into neurodevelopmental disorder research. Availability and implementation All Python codes associated with this study are available at https://github.com/ZL-Xia/RECGOBD.git. Supplementary information Supplementary data are available at Bioinformatics Advance online

Posttranslational modifications in retinal degeneration diseases: An update on the molecular basis and treatment

AbstractNoninherited diseases and age‐associated vision loss are often associated with retinal degeneration. The retina is a postmitotic neural tissue lacking endogenous regeneration capacity. Therefore, understanding the mechanism of retinal degeneration in diseases is pivotal. Posttranslational modifications (PTMs) determine protein function during physiological and pathological processes, including signal transduction, protein localization, and protein activation. Advanced detection technologies have revealed over 400 different PTMs including acetylation, methylation, phosphorylation, ubiquitination and SUMOylation. Here, we discuss PTMs in retinal degeneration diseases to aid in our understanding of their molecular basis and suggest potential future clinical treatment.

Proteome-scale tagging and functional screening in mammalian cells by ORFtag

The systematic determination of protein function is a key goal of modern biology, but remains challenging with current approaches. Here we present ORFtag, a versatile, cost-effective and highly efficient method for the massively parallel tagging and functional interrogation of proteins at the proteome scale. ORFtag uses retroviral vectors bearing a promoter, peptide tag and splice donor to generate fusions between the tag and endogenous open reading frames (ORFs). We demonstrate the utility of ORFtag through functional screens for transcriptional activators, repressors and posttranscriptional regulators in mouse embryonic stem cells. Each screen recovers known and identifies new regulators, including long ORFs inaccessible by other methods. Among other hits, we find that Zfp574 is a highly selective transcriptional activator and that oncogenic fusions often function as transactivators.

Abstract 2348: A global multiscale map of protein assemblies from integration of protein interactions and images

Abstract Cells regulate growth and function through a hierarchical structure of subcellular protein assemblies, in which alterations can result in cellular dysfunction leading to disease such as cancer. Much of this structure remains uncharted, resulting in recent efforts to map subcellular organization at different physical scales. Here, we report a global architectural map of human cancer cell protein assemblies spanning 10−9 to 10−5 nm, based on integration of near-proteome-wide affinity purification mass spectrometry-based protein interactions and immunofluorescent imaging in U-2 OS osteosarcoma cancer cells. The U-2 OS multi-scale integrated cell map places >5000 proteins into 270 distinct subcellular protein assemblies across biological scales, representing approximately half of expressed proteins. There are known organelles and protein complexes recovered in the map, as well as 152 putative assemblies, such as a putative interferon signaling complex consisting of a serine protease and STAT transcription factors. The map also incorporates 128 previously uncharacterized proteins into protein assemblies, such as C18orf21 association with a canonical RNAse complex. Protein subsystems in the U-2 OS map were evaluated for the potential for downstream integrative structural modeling, where available structural data (e.g. crosslinking, Protein Data Bank structures, AlphaFold predictions) are combined in a Bayesian framework to model the physical structures of protein complexes. Using this framework, we created an integrated structural model of new proteins interacting with the Rag-Ragulator complex, which regulates MAPK and mTOR signaling. In summary, the global proteome architecture map provides a resource for cellular biology discovery and the systematic determination of protein functions and structures. Citation Format: Leah V. Schaffer, Mengzhou Hu, Edward L. Huttlin, Gege Qian, Abantika Pal, Neelesh Soni, Andrew Latham, Laura Pontano Vaites, Trang Le, Yue Qin, Christopher Churas, Dexter Pratt, Ignacia Echeverria, Andrej Sali, J Wade Harper, Steven P. Gygi, Emma Lundberg, Trey Ideker. A global multiscale map of protein assemblies from integration of protein interactions and images [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2348.

Protein design using structure-based residue preferences

Recent developments in protein design rely on large neural networks with up to 100s of millions of parameters, yet it is unclear which residue dependencies are critical for determining protein function. Here, we show that amino acid preferences at individual residues—without accounting for mutation interactions—explain much and sometimes virtually all of the combinatorial mutation effects across 8 datasets (R2 ~ 78-98%). Hence, few observations (~100 times the number of mutated residues) enable accurate prediction of held-out variant effects (Pearson r > 0.80). We hypothesized that the local structural contexts around a residue could be sufficient to predict mutation preferences, and develop an unsupervised approach termed CoVES (Combinatorial Variant Effects from Structure). Our results suggest that CoVES outperforms not just model-free methods but also similarly to complex models for creating functional and diverse protein variants. CoVES offers an effective alternative to complicated models for identifying functional protein mutations.

The Function, Regulation, and Mechanism of Protein Turnover in Circadian Systems in Neurospora and Other Species.

Circadian clocks drive a large array of physiological and behavioral activities. At the molecular level, circadian clocks are composed of positive and negative elements that form core oscillators generating the basic circadian rhythms. Over the course of the circadian period, circadian negative proteins undergo progressive hyperphosphorylation and eventually degrade, and their stability is finely controlled by complex post-translational pathways, including protein modifications, genetic codon preference, protein-protein interactions, chaperon-dependent conformation maintenance, degradation, etc. The effects of phosphorylation on the stability of circadian clock proteins are crucial for precisely determining protein function and turnover, and it has been proposed that the phosphorylation of core circadian clock proteins is tightly correlated with the circadian period. Nonetheless, recent studies have challenged this view. In this review, we summarize the research progress regarding the function, regulation, and mechanism of protein stability in the circadian clock systems of multiple model organisms, with an emphasis on Neurospora crassa, in which circadian mechanisms have been extensively investigated. Elucidation of the highly complex and dynamic regulation of protein stability in circadian clock networks would greatly benefit the integrated understanding of the function, regulation, and mechanism of protein stability in a wide spectrum of other biological processes.

Learning the shape of protein microenvironments with a holographic convolutional neural network

Proteins play a central role in biology from immune recognition to brain activity. While major advances in machine learning have improved our ability to predict protein structure from sequence, determining protein function from its sequence or structure remains a major challenge. Here, we introduce holographic convolutional neural network (H-CNN) for proteins, which is a physically motivated machine learning approach to model amino acid preferences in protein structures. H-CNN reflects physical interactions in a protein structure and recapitulates the functional information stored in evolutionary data. H-CNN accurately predicts the impact of mutations on protein stability and binding of protein complexes. Our interpretable computational model for protein structure-function maps could guide design of novel proteins with desired function.

Employing Machine Learning Techniques to Detect Protein Function: A Survey, Experimental, and Empirical Evaluations.

This review article delves deeply into the various machine learning (ML) methods and algorithms employed in discerning protein functions. Each method discussed is assessed for its efficacy, limitations, potential improvements, and future prospects. We present an innovative hierarchical classification system that arranges algorithms into intricate categories and unique techniques. This taxonomy is based on a tri-level hierarchy, starting with the methodology category and narrowing down to specific techniques. Such a framework allows for a structured and comprehensive classification of algorithms, assisting researchers in understanding the interrelationships among diverse algorithms and techniques. The study incorporates both empirical and experimental evaluations to differentiate between the techniques. The empirical evaluation ranks the techniques based on four criteria. The experimental assessments rank: (1) individual techniques under the same methodology subcategory, (2) different sub-categories within the same category, and (3) the broad categories themselves. Integrating the innovative methodological classification, empirical findings, and experimental assessments, the article offers a well-rounded understanding of ML strategies in protein function identification. The paper also explores techniques for multi-task and multi-label detection of protein functions, in addition to focusing on single-task methods. Moreover, the paper sheds light on the future avenues of ML in protein function determination.

Abstract 16909: Eicosapentaenoic Acid and Docosahexaenoic Acid Have Competing Effects on Membrane Lipid Dynamics Due to Differences in Structure

Background: Omega-3 fatty acids (n3-FAs) incorporate into vascular cell membranes where they modulate protein function and signal transduction. The n3-FA eicosapentaenoic acid (EPA) delivered in ethyl ester form (icosapent ethyl) reduced a broad range of cardiovascular events in high-risk patients (REDUCE-IT). This was not observed with mixed n3-FA formulations containing docosahexaenoic acid (DHA) in STRENGTH and other trials. These discordant outcomes indicate DHA may counter certain EPA effects due to its additional carbons and double bond. We compared these n3-FA effects on membrane lipid dynamics and width, key determinants of protein function. Methods: Membrane lipid fluidity was measured by fluorescence anisotropy approaches in large unilamellar vesicles (LUVs) of 100 nm diameter containing 1-palmitoyl-2-oleoyl-3-sn-phosphatidylcholine (POPC) and 50 mol% cholesterol with EPA and/or DHA (0-10 mol%). LUVs were treated with the fluorescent probe, 1,6-diphenyl-1,3,5-hexatriene (DPH), at 1 mol% and evaluated using a spectrofluorometer with a polarization accessory. Membrane width ( i.e ., unit cell periodicity) of the samples was determined by small angle x-ray scattering (SAXS). Results: DHA alone increased bulk lipid fluidity as indicated by a dose-dependent reduction in apparent rotational correlation time (ARCT); at 10 mol%, there was a ~20% (p<0.05) reduction from ~19 to ~16 nsec compared to control (untreated membranes). By contrast, EPA had no significant disordering effect except when combined at equimolar levels; the EPA/DHA combination showed a reduction in ARCT to ~18 nsec at 10 mol% that was different from EPA alone (p<0.05). Consistent with these findings, SAXS analysis showed DHA containing membranes had a reduced width compared to EPA by 5% or 3 Å (p<0.05) due to increased trans-gauche isomerizations in the phospholipid acyl chains. Conclusion: EPA maintained membrane fluidity while DHA increased fluidity and thereby reduced membrane width due to its additional carbons and double bond. The EPA/DHA combination increased membrane fluidity compared to control and EPA alone. The disordering effects of DHA may counter EPA stabilizing effects and explain discordant clinical outcomes with different n3-FA formulations.

An Overview of Protein Function Prediction Methods: A Deep Learning Perspective

Abstract: Predicting the function of proteins is a major challenge in the scientific community, particularly in the post-genomic era. Traditional methods of determining protein functions, such as experiments, are accurate but can be resource-intensive and time-consuming. The development of Next Generation Sequencing (NGS) techniques has led to the production of a large number of new protein sequences, which has increased the gap between available raw sequences and verified annotated sequences. To address this gap, automated protein function prediction (AFP) techniques have been developed as a faster and more cost-effective alternative, aiming to maintain the same accuracy level. : Several automatic computational methods for protein function prediction have recently been developed and proposed. This paper reviews the best-performing AFP methods presented in the last decade and analyzes their improvements over time to identify the most promising strategies for future methods. : Identifying the most effective method for predicting protein function is still a challenge. The Critical Assessment of Functional Annotation (CAFA) has established an international standard for evaluating and comparing the performance of various protein function prediction methods. In this study, we analyze the best-performing methods identified in recent editions of CAFA. These methods are divided into five categories based on their principles of operation: sequence-based, structure-based, combined-based, ML-based and embeddings-based. : After conducting a comprehensive analysis of the various protein function prediction methods, we observe that there has been a steady improvement in the accuracy of predictions over time, mainly due to the implementation of machine learning techniques. The present trend suggests that all the bestperforming methods will use machine learning to improve their accuracy in the future. : We highlight the positive impact that the use of machine learning (ML) has had on protein function prediction. Most recent methods developed in this area use ML, demonstrating its importance in analyzing biological information and making predictions. Despite these improvements in accuracy, there is still a significant gap compared with experimental evidence. The use of new approaches based on Deep Learning (DL) techniques will probably be necessary to close this gap, and while significant progress has been made in this area, there is still more work to be done to fully realize the potential of DL.

Engineered ATG8-binding motif-based selective autophagy to degrade proteins and organelles in planta.

Protein-targeting technologies represent essential approaches in biological research. Protein knockdown tools developed recently in mammalian cells by exploiting natural degradation mechanisms allow for precise determination of protein function and discovery of degrader-type drugs. However, no method to directly target endogenous proteins for degradation is currently available in plants. Here, we describe a novel method for targeted protein clearance by engineering an autophagy receptor with a binder to provide target specificity and an ATG8-binding motif (AIM) to link the targets to nascent autophagosomes, thus harnessing the autophagy machinery for degradation. We demonstrate its specificity and broad potentials by degrading various fluorescence-tagged proteins, including cytosolic mCherry, the nucleus-localized bZIP transcription factor TGA5, and the plasma membrane-anchored brassinosteroid receptor BRI1, as well as fluorescence-coated peroxisomes, using a tobacco-based transient expression system. Stable expression of AIM-based autophagy receptors in Arabidopsis further confirms the feasibility of this approach in selective autophagy of endogenous proteins. With its wide substrate scope and its specificity, our concept of engineered AIM-based selective autophagy could provide a convenient and robust research tool for manipulating endogenous proteins in plants and may open an avenue toward degradation of cytoplasmic components other than proteins inplant research.

Protein function annotation based on heterogeneous biological networks

BackgroundAccurate annotation of protein function is the key to understanding life at the molecular level and has great implications for biomedicine and pharmaceuticals. The rapid developments of high-throughput technologies have generated huge amounts of protein–protein interaction (PPI) data, which prompts the emergence of computational methods to determine protein function. Plagued by errors and noises hidden in PPI data, these computational methods have undertaken to focus on the prediction of functions by integrating the topology of protein interaction networks and multi-source biological data. Despite effective improvement of these computational methods, it is still challenging to build a suitable network model for integrating multiplex biological data.ResultsIn this paper, we constructed a heterogeneous biological network by initially integrating original protein interaction networks, protein-domain association data and protein complexes. To prove the effectiveness of the heterogeneous biological network, we applied the propagation algorithm on this network, and proposed a novel iterative model, named Propagate on Heterogeneous Biological Networks (PHN) to score and rank functions in descending order from all functional partners, Finally, we picked out top L of these predicted functions as candidates to annotate the target protein. Our comprehensive experimental results demonstrated that PHN outperformed seven other competing approaches using cross-validation. Experimental results indicated that PHN performs significantly better than competing methods and improves the Area Under the Receiver-Operating Curve (AUROC) in Biological Process (BP), Molecular Function (MF) and Cellular Components (CC) by no less than 33%, 15% and 28%, respectively.ConclusionsWe demonstrated that integrating multi-source data into a heterogeneous biological network can preserve the complex relationship among multiplex biological data and improve the prediction accuracy of protein function by getting rid of the constraints of errors in PPI networks effectively. PHN, our proposed method, is effective for protein function prediction.

GRaSP-web: a machine learning strategy to predict binding sites based on residue neighborhood graphs.

Proteins are essential macromolecules for the maintenance of living systems. Many of them perform their function by interacting with other molecules in regions called binding sites. The identification and characterization of these regions are of fundamental importance to determine protein function, being a fundamental step in processes such as drug design and discovery. However, identifying such binding regions is not trivial due to the drawbacks of experimental methods, which are costly and time-consuming. Here we propose GRaSP-web, a web server that uses GRaSP (Graph-based Residue neighborhood Strategy to Predict binding sites), a residue-centric method based on graphs that uses machine learning to predict putative ligand binding site residues. The method outperformed 6 state-of-the-art residue-centric methods (MCC of 0.61). Also, GRaSP-web is scalable as it takes 10-20 seconds to predict binding sites for a protein complex (the state-of-the-art residue-centric method takes 2-5h on the average). It proved to be consistent in predicting binding sites for bound/unbound structures (MCC 0.61 for both) and for a large dataset of multi-chain proteins (4500 entries, MCC 0.61). GRaSPWeb is freely available at https://grasp.ufv.br.

RosettaSurf-A surface-centric computational design approach.

Proteins are typically represented by discrete atomic coordinates providing an accessible framework to describe different conformations. However, in some fields proteins are more accurately represented as near-continuous surfaces, as these are imprinted with geometric (shape) and chemical (electrostatics) features of the underlying protein structure. Protein surfaces are dependent on their chemical composition and, ultimately determine protein function, acting as the interface that engages in interactions with other molecules. In the past, such representations were utilized to compare protein structures on global and local scales and have shed light on functional properties of proteins. Here we describe RosettaSurf, a surface-centric computational design protocol, that focuses on the molecular surface shape and electrostatic properties as means for protein engineering, offering a unique approach for the design of proteins and their functions. The RosettaSurf protocol combines the explicit optimization of molecular surface features with a global scoring function during the sequence design process, diverging from the typical design approaches that rely solely on an energy scoring function. With this computational approach, we attempt to address a fundamental problem in protein design related to the design of functional sites in proteins, even when structurally similar templates are absent in the characterized structural repertoire. Surface-centric design exploits the premise that molecular surfaces are, to a certain extent, independent of the underlying sequence and backbone configuration, meaning that different sequences in different proteins may present similar surfaces. We benchmarked RosettaSurf on various sequence recovery datasets and showcased its design capabilities by generating epitope mimics that were biochemically validated. Overall, our results indicate that the explicit optimization of surface features may lead to new routes for the design of functional proteins.

PWM2Vec: An Efficient Embedding Approach for Viral Host Specification from Coronavirus Spike Sequences.

Simple SummaryThe family of coronaviruses comprises a diverse set of strains and variants which cause diseases from the common cold to COVID-19. Moreover, they infect a wide array of hosts from bats, camels, birds, to humans. Studying coronaviruses through the lens of host specificity provides a unique perspective to understanding the evolution, diversity and dynamics of this family. In particular, this can reveal groups of different hosts infected by similar strains, giving clues on strains which were more likely to have evolved to jump from one host to another. In this work, we frame host specificity as a classification task, in designing a very compact numerical representation of the spike sequences of different coronaviruses. Based on this numerical representation, classification methods are able to detect the target host with high accuracy. Such an approach can used to efficiently scale to large volumes of sequences, in order to unveil trends in the host specificity of different coronavirus strains.The study of host specificity has important connections to the question about the origin of SARS-CoV-2 in humans which led to the COVID-19 pandemic—an important open question. There are speculations that bats are a possible origin. Likewise, there are many closely related (corona)viruses, such as SARS, which was found to be transmitted through civets. The study of the different hosts which can be potential carriers and transmitters of deadly viruses to humans is crucial to understanding, mitigating, and preventing current and future pandemics. In coronaviruses, the surface (S) protein, or spike protein, is important in determining host specificity, since it is the point of contact between the virus and the host cell membrane. In this paper, we classify the hosts of over five thousand coronaviruses from their spike protein sequences, segregating them into clusters of distinct hosts among birds, bats, camels, swine, humans, and weasels, to name a few. We propose a feature embedding based on the well-known position weight matrix (PWM), which we call PWM2Vec, and we use it to generate feature vectors from the spike protein sequences of these coronaviruses. While our embedding is inspired by the success of PWMs in biological applications, such as determining protein function and identifying transcription factor binding sites, we are the first (to the best of our knowledge) to use PWMs from viral sequences to generate fixed-length feature vector representations, and use them in the context of host classification. The results on real world data show that when using PWM2Vec, machine learning classifiers are able to perform comparably to the baseline models in terms of predictive performance and runtime—in some cases, the performance is better. We also measure the importance of different amino acids using information gain to show the amino acids which are important for predicting the host of a given coronavirus. Finally, we perform some statistical analyses on these results to show that our embedding is more compact than the embeddings of the baseline models.

Regulation of Gramicidin Channel Function Solely by Changes in Lipid Intrinsic Curvature.

Membrane protein function is regulated by the lipid bilayer composition. In many cases the changes in function correlate with changes in the lipid intrinsic curvature (c0), and c0 is considered a determinant of protein function. Yet, water-soluble amphiphiles that cause either negative or positive changes in curvature have similar effects on membrane protein function, showing that changes in lipid bilayer properties other than c0 are important—and may be dominant. To further investigate the mechanisms underlying the bilayer regulation of protein function, we examined how maneuvers that alter phospholipid head groups effective “size”—and thereby c0—alter gramicidin (gA) channel function. Using dioleoylphospholipids and planar bilayers, we varied the head groups’ physical volume and the electrostatic repulsion among head groups (and thus their effective size). When 1,2-dioleyol-sn-glycero-3-phosphocholine (DOPC), was replaced by 1,2-dioleyol-sn-glycero-3-phosphoethanolamine (DOPE) with a smaller head group (causing a more negative c0), the channel lifetime (τ) is decreased. When the pH of the solution bathing a 1,2-dioleyol-sn-glycero-3-phosphoserine (DOPS) bilayer is decreased from 7 to 3 (causing decreased head group repulsion and a more negative c0), τ is decreased. When some DOPS head groups are replaced by zwitterionic head groups, τ is similarly decreased. These effects do not depend on the sign of the change in surface charge. In DOPE:DOPC (3:1) bilayers, pH changes from 5→9 to 5→0 (both increasing head group electrostatic repulsion, thereby causing a less negative c0) both increase τ. Nor do the effects depend on the use of planar, hydrocarbon-containing bilayers, as similar changes were observed in hydrocarbon-free lipid vesicles. Altering the interactions among phospholipid head groups may alter also other bilayer properties such as thickness or elastic moduli. Such changes could be excluded using capacitance measurements and single channel measurements on gA channels of different lengths. We conclude that changes gA channel function caused by changes in head group effective size can be predicted from the expected changes in c0.

A Neural Network-Based Multi-Label Classifier for Protein Function Prediction

Knowledge of the functions of proteins plays a vital role in gaining a deep insight into many biological studies. However, wet lab determination of protein function is prohibitively laborious, time-consuming, and costly. These challenges have created opportunities for automated prediction of protein functions, and many computational techniques have been explored. These techniques entail excessive computational resources and turnaround times. The current study compares the performance of various neural networks on predicting protein function. These networks were trained and tested on a large dataset of reviewed protein entries from nine bacterial phyla, obtained from the Universal Protein Resource Knowledgebase (UniProtKB). Each protein instance was associated with multiple terms of the molecular function of Gene Ontology (GO), making the problem a multilabel classification one. The results in this dataset showed the superior performance of single-layer neural networks having a modest number of neurons. Moreover, a useful set of features that can be deployed for efficient protein function prediction was discovered.

Construction of Mitochondrial Protection and Monitoring Model of Lon Protease Based on Machine Learning under Myocardial Ischemia Environment.

The localization of a protein's submitochondrial structure is important for therapeutic design of associated disorders caused by mitochondrial abnormalities because many human diseases are directly tied to mitochondria. When Lon protease expression changes, glycolysis replaces respiratory metabolism in the cell, which is a common occurrence in cancer cells. The fact that protein formation is a dynamic research object makes it impossible to reproduce the unique living environment of proteins in an experimental setting, which surely makes it more challenging to determine protein function through experiments. This research suggests a model of Lon protease-based mitochondrial protection under myocardial ischemia based on ML (machine learning). To ensure the balance of all submitochondrial proteins, the data set is processed using a random oversampling method, each overlapping fixed-length subsequence that is created from the protein sequence functions as a channel in the convolution layer. The results demonstrate that applying the oversampling strategy increases the ROC value by 17.6%-21.3%. Our prediction method is successful as evidenced by the fact that ML prediction outperforms the predictions of other conventional classifiers.

KRSA: An R package and R Shiny web application for an end-to-end upstream kinase analysis of kinome array data.

Phosphorylation by serine-threonine and tyrosine kinases is critical for determining protein function. Array-based platforms for measuring reporter peptide signal levels allow for differential phosphorylation analysis between conditions for distinct active kinases. Peptide array technologies like the PamStation12 from PamGene allow for generating high-throughput, multi-dimensional, and complex functional proteomics data. As the adoption rate of such technologies increases, there is an imperative need for software tools that streamline the process of analyzing such data. We present Kinome Random Sampling Analyzer (KRSA), an R package and R Shiny web-application for analyzing kinome array data to help users better understand the patterns of functional proteomics in complex biological systems. KRSA is an All-In-One tool that reads, formats, fits models, analyzes, and visualizes PamStation12 kinome data. While the underlying algorithm has been experimentally validated in previous publications, we demonstrate KRSA workflow on dorsolateral prefrontal cortex (DLPFC) in male (n = 3) and female (n = 3) subjects to identify differential phosphorylation signatures and upstream kinase activity. Kinase activity differences between males and females were compared to a previously published kinome dataset (11 female and 7 male subjects) which showed similar global phosphorylation signals patterns.

Membrane Domain Localization and Interaction of the Prion-Family Proteins, Prion and Shadoo with Calnexin.

The cellular prion protein (PrPC) is renowned for its infectious conformational isoform PrPSc, capable of templating subsequent conversions of healthy PrPCs and thus triggering the group of incurable diseases known as transmissible spongiform encephalopathies. Besides this mechanism not being fully uncovered, the protein’s physiological role is also elusive. PrPC and its newest, less understood paralog Shadoo are glycosylphosphatidylinositol-anchored proteins highly expressed in the central nervous system. While they share some attributes and neuroprotective actions, opposing roles have also been reported for the two; however, the amount of data about their exact functions is lacking. Protein–protein interactions and membrane microdomain localizations are key determinants of protein function. Accurate identification of these functions for a membrane protein, however, can become biased due to interactions occurring during sample processing. To avoid such artifacts, we apply a non-detergent-based membrane-fractionation approach to study the prion protein and Shadoo. We show that the two proteins occupy similarly raft and non-raft membrane fractions when expressed in N2a cells and that both proteins pull down the chaperone calnexin in both rafts and non-rafts. These indicate their possible binding to calnexin in both types of membrane domains, which might be a necessary requisite to aid the inherently unstable native conformation during their lifetime.